A Review of Endoscopic Ultrasound-Guided Chemoablative Techniques for Pancreatic Cystic Lesions

Pancreatic cystic lesions (PCLs) are known precursors to pancreatic cancer, one of the deadliest types of cancer worldwide. Surgical removal or pancreatectomies remain the central approach to managing precancerous high-risk PCLs. Endoscopic ultrasound (EUS)-guided therapeutic management of PCLs is a novel management strategy for patients with prohibitive surgical risks. Various ablation techniques have been explored in previous studies utilizing EUS-guided fine needle injection (FNI) of alcohol and chemotherapeutic agents. This review article focuses on EUS-FNI and chemoablation, encompassing the evolution of chemoablation, pancreatic cyst selection, chemotherapy drug selection, including novel agents, and a discussion of its safety and efficacy

Structure-based protein function prediction using graph convolutional networks

The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/

A Comparison of Single Dimension and Volume Measurements in the Risk Stratification of Pancreatic Cystic Lesions

The incidence of pancreatic cystic lesions (PCLs) has been rising due to improvements in imaging. Of these, intraductal papillary mucinous neoplasms (IPMNs) are the most common and are thought to contribute to almost 20% of pancreatic adenocarcinomas. All major society guidelines for the management of IPMNs use size defined by maximum diameter as the primary determinant of whether surveillance or surgical resection is recommended. However, there is no consensus on how these measurements should be obtained or whether a single imaging modality is superior. Furthermore, the largest diameter may fail to capture the complexity of PCLs, as most are not perfectly spherical. This article reviews current PCL measurement techniques in CT, MRI, and EUS and posits volume as a possible alternative to the largest diameter

Structure-based protein function prediction using graph convolutional networks.

The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/

Sequence-structure-function relationships in the microbial protein universe

For the past half-century, structural biologists relied on the notion that similar protein sequences give rise to similar structures and functions. While this assumption has driven research to explore certain parts of the protein universe, it disregards spaces that don't rely on this assumption. Here we explore areas of the protein universe where similar protein functions can be achieved by different sequences and different structures. We predict similar to 200,000 structures for diverse protein sequences from 1,003 representative genomes across the microbial tree of life and annotate them functionally on a per-residue basis. Structure prediction is accomplished using the World Community Grid, a large-scale citizen science initiative. The resulting database of structural models is complementary to the AlphaFold database, with regards to domains of life as well as sequence diversity and sequence length. We identify 148 novel folds and describe examples where we map specific functions to structural motifs. We also show that the structural space is continuous and largely saturated, highlighting the need for a shift in focus across all branches of biology, from obtaining structures to putting them into context and from sequence-based to sequence-structure-function based meta-omics analyses.Advances in protein structure prediction have led to a significant influx of protein structure data. Here the authors exploit this data to offer an unbiased overview of complex sequence-structure-function relationships in the protein universe. This work opens up new uses for 3D structure data repositories in meta-omics and other fields of biology.Peer reviewe

Coherent magnetic structures in terbium/holmium superlattices

Neutron-scattering techniques have been used to investigate the magnetic properties of three Tb/Ho superlattices grown by molecular-beam epitaxy. It is revealed that for temperatures in the range T = 10 to T-N(Ho)approximate to 130 K, there is a basal-plane ferromagnetic alignment of Tb moments within To blocks that is coherent with a basal-plane helical ordering of Ho moments. Between T approximate to T-N(Ho) and 200 K, the Tb moments remain ferromagnetically aligned within To blocks, with adjacent To blocks antiferromagnetically coupled. As the temperature is raised from T approximate to 200 to 230 K, two samples retain this magnetic structure while the third undergoes a transition first to a mixed phase of helically and ferromagnetically ordered Tb moments, then to a phase with only helically ordered To moments. Ln all cases, the magnetic ordering is found to be long ranged, with coherence lengths extending over three to six bilayers. The results are discussed with a consideration of previous rare-earth superlattice studies, and the possible mechanisms for interlayer coupling

Structural and magnetic properties of holmium-scandium alloys and superlattices

The properties of Ho-Sc alloys and superlattices grown by molecular-beam epitaxy have been investigated using x-ray and neutron-diffraction techniques. Structural studies reveal that the alloy samples have different a lattice parameters for the Sc-seed layer and the Ho:Sc alloy grown on top of the seed layer; while the superlattices have different a lattice parameters for the Sc seed, and for both the Ho and Sc in the superlattice layers. The structural characteristics are related to the large lattice mismatches (of the order 7%) between the constituent elements. The magnetic moments in the alloys form a basal-plane helix at all temperatures, with distortions of the helical arrangement for samples with the highest Ho concentrations. The dependences of the Neel temperature, T-N and the helical wave vector upon both temperature and concentration are compared with those of other alloy systems. It is found that a good description of the dependence of T-N upon concentration is given by a virtual-crystal model where the peak in the conduction-electron susceptibility varies linearly between that of the pure constituents. In the superlattices, the moments also form a basal-plane helix at T-N. In this helical phase, some samples exhibit a short-range coherence of an antiferromagnetic coupling between adjacent Ho blocks. For one superlattice, there is a low-temperature transition to a ferromagnetic phase, in which moments are ferromagnetically aligned within Ho blocks, and coupled antiferromagnetically between adjacent Ho blocks. The contrast with systems which have Y or Lu as the nonmagnetic element is discussed in terms of the structural properties of the samples, band-structure calculations, and the possible influence of dipolar forces